The vast majority of traders obsess over the percent accuracy of their expert advisors. Intuition makes it seem like that the more often a trader wins, the greater the chances or turning a profit. Alas, such an approach ignores a critical variable.

The average win-loss ratio plays an equally vital role in determining the net outcome. I meet a lot of would be scalpers. High frequency trading is incredibly popular, but a lot of traders involved with it only do so because it puts easy points on the board. They don’t pursue a strategy because it has any positive expectation. In other words, they are gambling and not trading.

One of the reasons that I love trading so much, and why I generally dislike gambling, is that you are always in control of the potential payout and the payout ratio. When I play blackjack, I only control the risk and payout. I do not control the ratio of the payout at all. It’s always 1:1.

My decisions in blackjack can only realistically improve the odds to 50%. More than likely, my game play will lower the odds below that threshold. Making decisions repeatedly will overwhelmingly result in human error. It’s our nature.

When I open my forex account, each trade commences a new round in the game. The critical difference between trading and blackjack is that I control the ratio of the payout, plus I still control the risk and quantity of the payout. The net outcome can still move against me due to random chance. The key distinction is that the typical outcome should shift in my favor with an algorithmic trading system.

One of my favorite trading books is Van Tharp’s Trade Your Way To Financial Freedom. We’ll be talking about this one soon; it’s the next item on Jon Rackley’s reading list. One of my favorite aspects of the book is its emphasis on money management strategies and trade expectation.

The term money management connotes many things to many people. The more accurate phrase would be to describe it as a position sizing strategy. When entering a trade, you realistically need to know:

What is expected loss as a percentage of the account?

What is the expected gain as a percentage of the account?

What is the percent accuracy of my trades?

Answering these questions accurately leads to the decision of how many lots, contracts or shares to trade. Controlling the size leads to controlling the outcomes. When you control the outcomes, you ideally earn a profit for your efforts.

Fixed fractional money management

Notice that I said percentage of the account in the bulleted items and not the dollar value of the trade. Thinking in terms of dollars is easier on the mind. The problems is that it ignores the wonderful benefits of exponential growth.

Every financial advisor on earth warns you that compound interest, which is a form of exponential growth, is the strongest force working for you with investments or against you with debts. Applying the same concept to trading, you want to put the power of compound growth on your side.

The fixed fractional formula is an ugly way to telling you to use exponential growth in your trading strategy. Say, for example, that you elect to risk 1% of the trading account based on the distance to the stop loss. If you have a $10,000 trading account, that’s only $100 of risk. Say that the trade works out and that you made $100. The next trade should risk $101.

Try not to roll your eyes at that one. Risking an extra dollar seems trivial and nit picky. I assure you that it is not.

I’m really not sure how to explain how all those little differences add up, but they do. I wrote a money management calculator a few years back that calculated how fixed fractional money management affects returns. The little things really do add up. With a very slight probability of winning and 50:50 odds, the returns were overwhelmingly larger when using a fixed fractional approach instead of a fixed lot approach. You should increase the position size after winners and decrease the position size after losers.

Percent accuracy is half important

If I paid you $1 for every win and you win 99% of the time, should you play my game?

You don’t have enough information to make a decision yet. You need to find out what happens when you lose.

If you lose $100 or more on the trade that only loses 1 time in 100, you should never play my game. You will lose if you play too often. And no, there is no such thing as just playing ten times and stopping. You have the same risk of losing on the first trade as you do on the 100th. It’s not safe to play at all.

The only way that you should decide to play the game is if the total payout is better than even. The total result of wins equals 99 trades * $1/trade = $99. The one loss must be less than $99 to give me the green light on playing.

If I lose $80 one time and make $99 on the remaining trials, then I will have an average win loss ratio of $99/$80 = 1.24. A system like this would be wildly in my favor.

A 60% winning accuracy is a lot more likely to happen in the trading world. Let’s say that I make $100 on every winning trade. My total winning value is 60 trades (out of 100) * $100/trade = $6,000. The maximum average loss that this system could tolerate is:

The maximum average loss that we can tolerate is $6,000 / 40 trades = $150. I should consider trading this system if the average loss comes in at $149 or less. The smaller the average loss, the greater the net outcome.

Kelly formula for Forex Trading

One problem we face with money management strategies is choosing the percentage of the account to risk. The difference between risking 1% or risking 2% of the account equity is simply one of proportion. One of the options either provides a risk-reward profile suitable to the trader or it doesn’t. The larger the appetite for risk and reward, the bigger the number involved.

The Kelly formula removes the proportionality for the question and takes a different approach: how do I make the absolute largest sum of money over time using my trading statistics. The goal is to make the maximum amount of money without getting margin called.

The formula to use is K = W – (1-W)/R where:

K = percentage of capital to be put into a single trade.
W = Historical winning percentage of a trading system.
R = Historical Average Win/Loss ratio.

The approach is most suitable for those trading small accounts, perhaps those with only a few thousand dollars, that they want to grow with maximum aggression. Losing a few dollars is thoroughly unpleasant (been there, done that!), but it’s not financially devastating, either.

It’s important to keep in mind that the Kelly formula attempts to push the trading system to its absolute maximum without busting. Knowing how close it is to the edge of busting, it’s critically important that you understate the good assumptions and overstate the bad ones. Drop the expected percent accuracy by several percentage points to accommodate the chance of error. Lower the win:loss ratio for the same reason.

The easiest way to reduce error and the chance of acting too aggressively is to make sure that you calculated the EA’s percent accuracy and its win loss ratio on a large enough sample size. I would consider 100 trades as the absolute bare minimum. 300-400 is sufficient. 1,000+ trades makes for an adequate sample for most expert advisors and trading robots.

Of course, you can always take the easier approach and simply cut the Kelly formula’s risk suggestion in half. It adds a bit of scientific flair to the strategy, while minding the fact that we are human. Watching an account drop near zero will break the heart of even the most battle tested trader. It’s impossible to stop caring about drawdown, which the Kelly formula totally ignores.

I set a reading list for new programming hires before they ever interact with customers. OneStepRemoved’s newest programmer, Jon Rackley, comes with an outstanding background in programming, but almost no market knowledge. Jon is under my wing for the next few months. My goal is to fully indoctrinate him on my market views.

One of the most useful, high level overview of trading dynamics is Benoit Mandelbrot’s The (Mis)behavior of Markets. My philosophy is that before you attack a problem, you first need to understand the problem. Building trading robots is the problem that our customers face every day. One reason that many would-be automated traders fail is that they understand very little about how markets function. They try to solve a problem that they don’t understand very well.

Mandelbot’s “10 heresies of finance” sums up my view of markets quite well. I made Jon read the book as part of his job training because has no trading experience. If you’re relatively new to markets or think it’s time to step back and re-evaluate your approach, then I highly recommend this book.

The 10 heresies of finance

Markets are turbulent.

Markets are far more risky than the standard theories imagine. Mandelbrot does do a very good job throughout his book explaining how unpredictable and complex market behavior really is.

Market timing matters greatly. Big gains and losses concentrate into small packages of time. He calls this trading time.

Prices often leap, not glide. That adds to the risk.

Time is flexible. Some times of day are more important than others

Markets in all places and ages work alike. Human behavior never changes, even if the mood does

Markets are inherently uncertain. Bubbles are inevitable. We will always be greedy

Markets are deceptive.

Forecasting prices may be perilous, but you can estimate the odds of future volatility

In financial markets, the idea of “value” has limited value.

Jon found Mandelbrot’s comparison between wind turbulence and market dynamics the most compelling part of his argument. It’s conceptually difficult to accept the idea of human behavior acting like the wind. The math of the two behaviors is identical, which is somewhat difficult to accept emotionally. What I like about the argument is that it makes for a beautiful metaphor.

When a storm comes up, it’s noteworthy. The average wind speed picks up. People notice that part. What everyone really notices and gossips about are the wind gusts. Staring out the window and watching the family oak tree bend a quarter way to the ground makes an impression.

Wind gusts are essentially market volatility. Events like a collapse of the euro or a surprise NFP number are the violent gusts of wind that make the jaws of traders everywhere drop to the ground. One gust begets another gust, packing themselves densely in time. Mandelbrot would reference the
increase in gusts as trading time speeding up.

If you enjoy these types of ideas, you might find our free resetting moving average indicator useful. It builds on Mandelbrot’s fractal approach to markets, negating the idea of a true average. The resetting moving average tries to act as a minimum reference point by changing its period in step with market volatility.

A client from Malaysia emailed yesterday asking about the merits of trading with EAs. He went through the usual motions of buying “quite a number of EAs [that] promised to make millions and ended up crashing or losing my live accounts.” He is not the first, nor will be the last to experience the roller coaster.

He watched a number of videos on YouTube that emphasize the importance of discretionary trading over automated forex robots. They claim, correctly, that a human being understands subtleties that computer programs miss. They then stretch the argument even further and say that EA trading is a fallacy. Given his bad luck, he wondered to what extent that I agree with the argument.

The wrong people are involved in selling EAs (usually)

The argument oversimplifies a difficult concept. Many of you know that I’m a self-confessed math geek. I like numbers. They are logical and they do not vary. What appeals so strongly to me about trading is that you can take a complex process and mold it into a set of logical rules to follow. It’s a wonderfully challenging problem. You’ll never crack it entirely, but I know from experience that you can develop something that works.

My gripe with most commercial EAs is that the people selling them are not traders. They are internet marketers that stumbled into a highly profitable niche industry. They tick the right boxes for their audience.

Most US forex traders are white men between the ages of 40-65 with an unusually high tendency to earn six figure incomes. The marketing gang knows that themes of financial security and the idea of an job that’s not miserable sells well. They focus more on getting that message across than developing a decent product.

EAs are only as good as their designers. OneStepRemoved has designed 1,000+ EAs. I’ve never seen anyone accidentally create a profitable strategy. The ones that do turn out well almost always have years or even decades of trading experience. The rules that they propose usually include subtle distinctions that differentiate between various market conditions and types of volatility.

Those types of people are not marketers. They spent their career trading. It’s part of the reason that the meagerly stocked talent pool of traders does not get come into contact with the marketing crew. The other problem is that the guys coming up with strategies worth selling have no incentive to do so.

Unless you’re FAP Turbo or MegaDroid, the potential income for most trading robot oriented business is six figures. I’ve worked with several multimillion dollar trading educators. They seem to do better in the long run owing to the relationships that they form with their customers. They are the exception rather than the rule.

Now look at how much a truly profitable trading strategy can make. Millions. No marketers or affiliates required. No web site. Just a computer and a server. There’s no effort required beyond passive monitoring. If you had a trading system that generated blockbuster returns, would you even bother with trying to form it into a business? I wouldn’t.

Traders like this do exist. It’s just that they’re about 0.01% of the trading population. I know this from my brokerage experience because once in a blue moon I fielded their phone calls. They were the perfect clients. They traded all the time, rarely called (only people losing money call their broker) and when they did, it was for some routine matter like withdrawing profits.

The only thing that stood out about them was their account balance. These people are the source of the dream, and they’re what keep the remaining 99.99% of algorithmic trading developers going.

Knowing how sweet the setup is if you have something worth selling, and also knowing that the financials really don’t make sense for selling a blockbuster system, I honestly would not consider purchasing an EA from the internet. I would never yield control over my account to something that behaves in a manner that I don’t understand.

When the drawdown inevitably comes, I would have no way to feel comfortable that the strategy is actually viable in the long haul. I would suspect that the end is nigh and that I better shut down the account before the losses accumulate. How anyone can make an informed financial decision with strictly marketing material is beyond my understanding.

What most expert advisors miss

Trading rules restrict discretionary traders in a way that I find beneficial. It cuts down on their tendency to over-trade, forcing them into situations where patience more often that not wins out.

Todd over at Triple Threat Trading runs a trading education business. He loves to go on and on about how he makes customers quantify their approaches as a set of rules. It’s easy enough to lose money in forex or futures.

The rule based approach minimizes those hazards. Following rules eliminates many of the unknown variables from your performance evaluation. Knowing that you followed the rules and still lost reduces the blow to the ego. It’s easier to say “the rules lost” instead of “I lost.”

The logic naturally flows into the idea of automating the strategy. The chief advantage is that the computer does not fatigue or tire. It follows the rules blindly. So long as a human remotely monitors the execution, the semi-automated approach often works well for established, rule based traders.

Making a trading robot does create new problems like deciding whether to only trade during active sessions or to leave the expert advisor on 24 hours a day. These are usually problems that resolve themselves with a few months of hands on experience.

The main lesson that I learned while running my forex account up this year with a gray box EA is how important it is to stay patient. Most of the systems that I trade personally tend to eat losses way too quickly. I have yet to determine how to quantify patience in trading terms, despite my love for quantifying things.

Certain rules do work dramatically better during different market conditions. The key is to separate the conditions, then apply the strategy selectively. Most expert advisor designers try to develop a strategy that works all the time.

It doesn’t work that way. When the EA starts to hit a wall on the design, the trader responds with new additions and tweaks to force the performance to improve the general performance.

What I’m working on now is a set of rules to identify various market types. I like to think of it as “don’t compare lawn mowers when it’s raining.” There’s a time and a place for each approach and behavior. The trick isn’t so much the rules, but deciding when to apply them.

Categorizing the market helps make the strategy selection process far easier. If the market is highly volatile and ranging, then select a scaling strategy that works well in ranging markets. If the market is trending quietly, then perhaps a basic trend strategy would work perfectly well.